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Tokenization in Private Equity: Broadening Markets with Sologenic
December 21, 2023
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The tokenization of private equity marks a profound shift in investment and blockchain technology. These tokens digitally represent ownership in private equity investments, utilizing decentralized ledgers. They facilitate fractional ownership, enhance liquidity, and streamline the management of private assets, effectively democratizing an asset class once known for its illiquidity and opacity.

Liquidity Democratized

Recent research underscores the growing interest in tokenizing private equity and hedge fund assets. A survey of fund managers across several European countries, responsible for managing approximately $546.5 billion in assets, found that 73% see private equity assets as the most likely to undergo significant tokenization. This trend is supported by estimates from the World Economic Forum, which predicts that up to 10% of global GDP could be stored and transacted via distributed ledger technology by 2027.

The financial benefits of private equity tokens are plenty, particularly in terms of liquidity. Tokenization enables these assets to trade on secondary markets, providing a more liquid alternative compared to traditional private equity investments. This gives investors the ability to enter and exit positions more easily and unlocks additional value in terms of usability and ease. Additionally, the inherent transparency provided by blockchain technology enhances trust and reduces risks associated with fraud and mismanagement.

Tokenization in OTC Markets

Over-the-counter (OTC) markets present another area where tokenization is making a significant impact. Traditionally known for trading unlisted securities that do not meet the requirements of stock exchanges, are often perceived as less transparent, opaque and exclusive markets.

The incorporation of blockchain technology in these markets is expected to enhance transparency, reduce transaction costs, and improve the efficiency of trade settlements. This technology will open up these markets to a wider audience, allowing for easier and more secure trading of a variety of assets, including those in private equity.

By establishing transaction records, capitalization tables, and smart contracts directly on the blockchain, trading in securities, including those in private equity, can be executed with unprecedented precision and minimal bureaucratic overhead.

Many of the processes involved in the trade and settlement of securities can be streamlined, notably reducing the need for intermediaries and thereby cutting transaction costs. A key aspect of this streamlining is the use of blockchain technology for escrow in OTC trades. Specifically, the XRP Ledger offers a robust mechanism for this purpose through its Trustlines feature. Trustlines on the XRPL enable the creation of secure and efficient escrow arrangements for trades. They facilitate the holding of assets until specific conditions are met, ensuring that all parties meet their obligations before the transaction is finalized.

This functionality not only enhances the security of trades by reducing counterparty risk but also significantly speeds up the settlement process. In traditional systems, escrow arrangements often involve several layers of verification and third-party involvement, which can be time-consuming and costly. The XRPL’s Trustline system, by contrast, automates and enforces these agreements directly on the blockchain, offering a more streamlined and cost-effective solution.

This advancement in escrow processing is particularly impactful in OTC markets, where the complexity and size of trades often necessitate robust escrow solutions. The ability to execute these essential components on the blockchain represents a major leap forward in making OTC trading more accessible, efficient, and secure.

Private Equity Tokenization Today

A few notable examples demonstrate the diversity and potential impact of these efforts:

Taurus and Teylor Partnership for Tokenizing SME Loans in Germany: Deutsche Bank-backed Taurus has partnered with Teylor, a fintech company, to tokenize small and medium enterprise (SME) loans in Germany. This collaboration aims to revolutionize the way SME loans are financed and traded. By tokenizing these loans, Taurus and Teylor are making it possible to trade these debt instruments on a blockchain platform, providing a new level of accessibility and liquidity to an otherwise traditional and less liquid asset class.

UK Publishes Regulations for Digital Securities Sandbox: The digital securities sandbox represents a significant step in the UK’s approach to integrating blockchain technology within its financial markets. Offering a controlled setting for testing, allows firms to explore the tokenization of assets, including private equity, without the full weight of regulatory compliance that would typically apply. This sandbox environment is crucial for identifying potential risks, understanding the impact of these technologies, and developing appropriate regulatory responses.

Republic’s Tokenized Venture Capital Funds: Republic, a platform known for democratizing investment opportunities, is set to make a significant mark in the field of private equity tokenization. The initiative involves creating digital tokens, $NOTE, that represent shares in a venture fund, making them available for trading on a digital securities platform. The tokenization of this fund is a pivotal step towards making venture capital investments more accessible to a wider range of investors.

Sologenic’s Tokenization Solutions for Private Equity

Sologenic’s role in the field of asset tokenization, particularly in private equity, aligns well with the growing trend of leveraging blockchain technology to transform traditional investment modelsSologenic, a sophisticated ecosystem built on the XRP Ledger, provides an innovative platform for the tokenization of a wide range of assets, including private equity.

  • Tokenization Capabilities of Sologenic: Sologenic’s ecosystem includes the ability to tokenize non-blockchain assets such as stocks, ETFs, commodities, and potentially private equity. This process involves converting these assets into digital tokens on the blockchain, enabling them to be traded on over 30 global stock exchanges against cryptocurrencies​
  • Controlled Transparency: The whitelisting feature on Smart Tokens within Coreum adds a layer of security and regulatory compliance. It enables the platform to effectively manage and control access to tokenized assets, ensuring that only verified and authorized participants can engage in transactions. This feature is crucial, especially in adhering to various regulatory requirements like Know Your Customer (KYC) and Anti-Money Laundering (AML) standards.
  • Regulatory and Technological Framework: As with the broader tokenized asset market, Sologenic’s platform operates within a rapidly evolving regulatory and technological landscape. Ensuring compliance with securities laws and navigating the complexities of blockchain technology is essential for the successful implementation of private equity tokenization.

“I’m observing a rapidly growing trend in the tokenization of private equity, and it’s evident that private equity fund managers are increasingly recognizing the need for enhanced liquidity, fractional ownership opportunities, and streamlined access to a broader pool of investors. Tokenization presents a transformative solution to address these crucial requirements, paving the way for a more dynamic and accessible private equity landscape.” — Michael McCaffrey, Business Development at Sologenic

Sologenic’s tokenization solutions, when applied to private equity, could significantly contribute to reshaping the investment landscape. With its robust technology and versatile platform, Sologenic is well-positioned to be a key player; aligning with global trends towards more accessible, transparent, and liquid investment opportunities. As the trend of tokenization continues to mature, Sologenic’s role in this field could become increasingly pivotal, leading to broader adoption and innovation in the RWA sector.

About Sologenic

Sologenic’s regulated arm is deploying a platform with a hybrid model for the on-demand tokenization of assets. This platform facilitates trading between crypto and off-chain traditional assets such as stocks & ETFs. The institutional-grade offering is designed for RIA’s, brokerage houses, family offices, banks and other financial institutions looking to tokenize real-world assets for their clients. Learn more at www.sologenic.com

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Revolut Leak Shows the Cost of Constant ID Collection
Revolut’s mistake is the news, but the bigger problem is the growing number of companies being encouraged or required to keep copies of our most sensitive identity documents.

Online bank Revolut has revealed that it gave out sensitive personal and financial information of an undisclosed number of its customers in response to a fake government request.

The information that was handed over to an “unauthorized third party” reportedly includes names, dates of birth, occupations, addresses, phone numbers, account numbers, transaction histories (including Bitcoin), and even copies of government-issued IDs and onboarding verification selfies.

Revolut claims that derived biometric face data was not.

The company said that the data was handed over in response to an email that came from a real government agency’s domain, but was not actually sent or authorized by that agency.

The email passed several authentication checks (SPF, DKIM, and DMARC) that are designed to establish the authenticity of a message’s origin and integrity, but do not verify the legitimacy of the legal request itself.

Revolut said that it complied with the request “under the reasonable belief that it was an authentic government agency request” – and only later found out that it was not.

Revolut said it later realized its mistake, blocked the email address, and reported the incident to the relevant authorities.

Revolut said that only a “limited” number of its customers were affected by the data leak, and that the company’s systems were not hacked, nor was any money stolen.

The story broke on September 11 when Revolut customers started receiving an email notice about a data leak, and the news was picked up by media outlets the following day.

Revolut notice explaining customer identity and financial data was shared after an unauthorized government email request.

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One reason for this is know your customer (KYC) and anti-money laundering (AML) rules. Revolut’s current UK customer privacy notice spells it out: the company generally keeps personal data of UK customers for no more than seven years after the relationship ends, and sometimes longer – for legal reasons.

This means that even if you close your account, your identity documents don’t disappear.

And while the incident with Revolut happened in the financial sector, it’s by no means the only one that requires customers to hand over sensitive identity information. Discord, a popular chat service, said in an October 9, 2025 security update that government ID photos of approximately 70,000 users may have been exposed after a third-party customer service provider got hacked.

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It’s hard to do anything about a copy of your old passport, or a photo of your face, or a record of your past transactions. These can be used to identify and profile you, and can be used to carry out targeted fraud. And this can happen even if the initial disclosure didn’t result in financial loss.

The more companies are forced to collect and store such information, and the more of it they have, the more opportunities there are for this data to be leaked, either by the company itself or a third party it works with. That's what makes governments' push for more ID checks just to access ordinary parts of life so reckless.

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This Is The Income A Family Needs To Live Comfortably In Every US State

Here’s the short version of what it takes for a family of four to live comfortably in 2026 by state:

In Massachusetts, you’d need nearly $330,000 a year - the highest figure in the entire country. Only three states clear the $300,000 mark: Massachusetts, Hawaii, and California. At the other end of the spectrum, Mississippi is the most affordable at about $188,000. That’s a full $142,000 less than what you’d need in Massachusetts.

So… how much does a family of four need in your state?

This map shows the pre-tax income a household with two working adults and two kids needs to live comfortably in every U.S. state.

The numbers come from SmartAsset (as of February 2026). They’re based on the familiar 50/30/20 budget: 50% for necessities, 30% for discretionary spending, and 20% for savings or other goals. These aren’t bare-minimum survival numbers—they’re what it takes to live pretty well while still putting money aside.

And as Visual Capitalist notesMassachusetts sits at the very top of that list. Massachusetts tops the ranking, with a family of four needing $329,555 per year to meet the 50/30/20 benchmark.

Hawaii follows at $313,165, while California ranks third at $302,682.

Rank State Income needed for family of four (2026)

  • 1 - Massachusetts - $329,555
  • 2 - Hawaii - $313,165
  • 3 - California - $302,682
  • 4 - Connecticut - $298,189
  • 5 - New Jersey - $295,110
  • 6 - New York - $291,533
  • 7 - Colorado - $283,213
  • 8 - Washington - $281,798
  • 9 - Oregon - $280,966
  • 10 - Vermont - $280,384
  • 11 - Alaska - $272,064
  • 12 - New Hampshire - $267,904
  • 13 - Rhode Island - $264,659
  • 14 - Minnesota - $263,078
  • 15 - Maryland - $257,837
  • 16 - Maine - $250,931
  • 17 - Montana - $249,434
  • 18 - Pennsylvania - $247,936
  • 19 - Illinois - $244,109
  • 20 - Virginia - $242,944
  • 21 - Nevada - $242,278
  • 22 - Indiana - $241,696
  • 23 - Wisconsin - $238,451
  • 24 - Arizona - $236,870
  • 25 - Utah - $235,789
  • 26 - Delaware - $228,134
  • 27 - Ohio - $226,221
  • 28 - Idaho - $226,054
  • 29 - Florida - $223,392
  • 30 - New Mexico - $223,142
  • 31 - Nebraska - $223,059
  • 32 - Missouri - $217,734
  • 33 - Georgia - $214,573
  • 34 - Michigan - $214,323
  • 35 - South Carolina - $212,909
  • 36 - North Carolina - $212,410
  • 37 - Wyoming - $212,410
  • 38 - Oklahoma - $211,910
  • 39 - North Dakota - $210,496
  • 40 - Kansas - $207,917
  • 41 - Iowa - $204,422
  • 42 - Texas - $203,424
  • 43 - West Virginia - $202,592
  • 44 - South Dakota - $201,760
  • 45 - Alabama - $198,931
  • 46 - Louisiana - $197,933
  • 47 - Tennessee - $197,267
  • 48 - Arkansas - $195,437
  • 49 - Kentucky - $194,854
  • 50 - Mississippi - $187,533

Connecticut, New Jersey, and New York aren't far behind, bringing the number of states with comfortable-income thresholds above $290,000 to six.

Colorado and Vermont Make the Top 10

As expected, many of the highest income thresholds are concentrated in the Northeast and along the West Coast.

However, Colorado has the seventh-highest threshold in the country at $283,213, ranking above Washington and Oregon.

Vermont rounds out the top 10 at $280,384, despite having the second-smallest population of any U.S. state. Meanwhile, nearby states like New Hampshire, Maine, and Rhode Island all fall outside the top 10.

Just Six States Come in Below $200,000

Despite the wide range in living costs across the country, only six states have a comfortable-income threshold below $200,000 for a family of four.

Mississippi ranks lowest at $187,533, followed by Kentucky. The states of Arkansas, Tennessee, Louisiana, and Alabama also fall below the $200,000 mark.

The gap between Massachusetts and Mississippi exceeds $142,000 per year, meaning the Massachusetts benchmark is about 76% higher.

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🤖Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?🤖
The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.
 
Over the last three years, a small group of companies has emerged as the backbone of the AI revolution. Microsoft provides cloud infrastructure. NVIDIA supplies the chips. Google, OpenAI, Anthropic, Meta, and others develop the models. Together, they control much of the compute, data, and software stack powering modern AI.
 
Now that AI is moving into the physical world, many are asking a bigger question:
 
Will these same companies end up controlling robotics too?
 
It's a valid concern.
 
The latest generation of robots relies on enormous amounts of compute, simulation, training data, and foundation models. Many robotics startups today are built on infrastructure provided by large technology companies. NVIDIA's Omniverse is becoming a key simulation environment for robot training. Microsoft Azure is powering the training of robotics foundation models. Physical AI startups increasingly depend on hyperscale cloud infrastructure to train and deploy intelligent systems. Recent partnerships across the industry show just how central Big Tech has become to robotics development.
But while Big Tech is building the highways, another movement is trying to ensure it doesn't own every destination.
 
That movement is decentralized AI.
 
Why Decentralized AI Exists
 
The idea behind decentralized AI is simple. Instead of a handful of companies owning the models, compute infrastructure, data pipelines, and intelligence networks, these resources are distributed across thousands of participants.
 
This means anyone can contribute compute, contribute models, validate outputs and can participate.
The most visible example today is the decentralized AI network known as Bittensor (@bittensor). The network has evolved into a large ecosystem of specialized AI markets called subnets, where participants compete to provide useful machine intelligence and are rewarded based on performance. Rather than relying on a single company, intelligence is generated and validated by a distributed network of miners and validators.
 
Think of it as an attempt to build an open marketplace for AI instead of a world where intelligence is rented from a few centralized providers.
 
Why This Matters for Robotics
 
Robotics has a unique problem. Unlike chatbots, robots operate in the physical world. They need to perceive environments, make decisions, move safely and they need to learn continuously.
 
The challenge is that collecting and training on real-world robotic data is incredibly expensive. That's one reason large companies have such an advantage. They can afford the compute, simulation environments, and data infrastructure needed to train robotics models at scale.
 
This is where decentralized systems become interesting.
 
Instead of one company collecting all the data and training all the models, decentralized networks could allow thousands of contributors to participate in building robotic intelligence.
 
Imagine a future where:
  • Warehouse robots contribute operational data.
  • Delivery robots contribute navigation data.
  • Factory robots contribute manipulation data.
  • Developers contribute models.
  • Validators evaluate performance.
The resulting intelligence becomes a shared network rather than a proprietary asset.
 
That vision is beginning to emerge.
 
Bittensor's Move Toward Physical AI
 
While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.
 
One example is Kinitro, a subnet focused on incentivizing the training and evaluation of embodied AI systems. The goal is to create competitive environments where developers build robotic intelligence and are rewarded based on performance.
 
The broader Bittensor ecosystem has also expanded into compute marketplaces, distributed inference systems, bandwidth infrastructure, and AI coordination layers that could eventually support robotics workloads. Several subnets now focus on decentralized compute, confidential inference, data transfer, and model training, critical components for future robotic systems.
 
In other words, the pieces are starting to appear.
 
Not a decentralized robot network yet.
 
But the infrastructure that could support one.
 
Beyond Bittensor: The Rise of Physical AI Networks
 
Bittensor isn't alone.
 
Across the industry, researchers and builders are experimenting with decentralized approaches to physical AI.
 
New research published in 2026 introduced the concept of DAO-enabled decentralized physical AI, or DePAI. The idea combines robotics, decentralized infrastructure, AI models, governance systems, and human oversight into a single framework. Instead of centralized control, robots and physical infrastructure could be coordinated through transparent rules and distributed ownership models.
 
At the same time, developers are exploring decentralized operating systems for robots that allow machines to communicate directly with each other and with distributed compute resources. These architectures are designed to make robotic systems more resilient and less dependent on a single cloud provider.
 
The goal is not simply decentralization for its own sake.
 
The goal is resilience.
 
If one server fails, the system continues.
 
If one company disappears, the network survives.
 
If one participant leaves, innovation continues.
 
But Here's the Reality
 
Decentralized AI faces the same challenge every decentralized technology faces.
 
Big Tech has resources. A lot of resources.
 
Training advanced robotics models requires enormous compute budgets, sophisticated simulation environments, access to specialized hardware, and vast amounts of real-world data.
 
That's why many robotics startups still partner with major cloud providers and AI companies. It's often the fastest path to deployment.
 
And there are legitimate concerns about whether decentralized networks can maintain quality, reliability, and security at the scale required for industrial robotics. Even researchers studying decentralized AI systems have highlighted risks around concentration, incentives, governance, and network security.
 
The challenge isn't just decentralizing intelligence.
 
It's decentralizing intelligence while maintaining performance.
 
That's much harder.
 
The Most Likely Outcome
 
The future probably won't be fully centralized. And it probably won't be fully decentralized either. Instead, we're likely heading toward a hybrid model.
 
Large technology companies will continue providing chips, cloud infrastructure, simulation platforms, and foundational research.
 
At the same time, decentralized AI networks will emerge as alternative coordination layers where intelligence, data, and economic value can be shared more openly.
 
The companies building robots may use NVIDIA hardware.
 
Train on Azure.
 
Run foundation models from OpenAI.
 
But they may also participate in decentralized data networks, decentralized compute markets, and decentralized intelligence protocols.
 
The future of robotics could end up looking less like a monopoly and more like an ecosystem.
 
The Bigger Question
 
The real question isn't whether decentralized AI can eliminate Big Tech.
 
It can't.
 
At least not anytime soon.
 
The real question is whether decentralized AI can prevent a future where a handful of companies control every robot, every model, every dataset, and every decision made by the machines operating around us.
 
As robots become workers, assistants, delivery drivers, factory operators, and even economic agents, that question becomes increasingly important.
 
Because the battle for the future of robotics is no longer about hardware.
 
It's about who owns the intelligence.
 
And that battle is just getting started.
 
 

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